mlsanityData to 14 Oct 2026

COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System

arXiv 2311.03753 ↗First posted 7 Nov 2023cs.AI cs.CL cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper explores the integration of neural networks with logic programming, addressing the longstanding challenges of combining the generalization and learning capabilities of neural networks with the precision of symbolic logic. Traditional attempts at this integration have been hampered by difficulties in initial data acquisition, the reliability of undertrained networks, and the complexity of reusing and augmenting trained models. To overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic) programming language, an innovative approach that seamlessly combines logical reasoning with neural network technologies. COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data. It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle to promote the reuse and augmentation of networks. Furthermore, the foundational principles and algorithms in COOL's design and its compilation system could provide valuable insights for future developments in programming languages and neural network architectures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 76 references that are in the map. First posted on arXiv 7 Nov 2023.

Builds on

14 of 76 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  5. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  6. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  7. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  8. 2017Neural-Symbolic Learning and Reasoning: A Survey and Interpretationcited by 196
  9. 2016Neuro-Symbolic Program Synthesiscited by 84
  10. 2020Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoningcited by 58
  11. 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
  12. 2018Neural-Guided Deductive Search for Real-Time Program Synthesis from Examplescited by 39
2 more
  1. 2016Survey of resampling techniques for improving classification performance in unbalanced datasetscited by 33
  2. 2018Neural Guided Constraint Logic Programming for Program Synthesiscited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Scallop: A Language for Neurosymbolic Programming
  2. 2024Neuro-symbolic Learning Yielding Logical Constraints
  3. 2023Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture
  4. 2022Extending Logical Neural Networks using First-Order Theories
  5. 2026From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS
  6. 2025Current Practices for Building LLM-Powered Reasoning Tools Are Ad Hoc -- and We Can Do Better
  7. 2025A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning
  8. 2022Evaluating Step-by-Step Reasoning through Symbolic Verification